{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a8f129b-8c1c-488d-9a1a-ad163f8d9cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.integrate import quad, IntegrationWarning\n",
    "import warnings\n",
    "# %matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc0edd8b-95ef-4284-9b0b-5965dc2f3a21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to load data from files\n",
    "def load_data(filename):\n",
    "    # Load the data assuming two columns: znew and rho, and skipping the header row\n",
    "    data = np.loadtxt(filename, skiprows=1)  # Skip the header row\n",
    "    znew = data[:, 0]\n",
    "    rho = data[:, 1]\n",
    "    return znew, rho\n",
    "\n",
    "# Function to create a callable function for rho\n",
    "def create_rho_func(znew, rho):\n",
    "    return lambda z: np.interp(z, znew, rho)\n",
    "\n",
    "# Define the integrand function\n",
    "def integrand(z, rho_func, rho_sub):\n",
    "    return rho_func(z) - rho_sub\n",
    "\n",
    "# Define the rho_2D function with increased subdivisions limit\n",
    "def rho_2D(rho_func, rho_sub, ll, ul, limit=100):\n",
    "   \n",
    "    with warnings.catch_warnings():\n",
    "        warnings.filterwarnings('ignore', category=IntegrationWarning)\n",
    "        result, _ = quad(integrand, ll, ul, args=(rho_func, rho_sub), limit=limit)\n",
    "    return result\n",
    "\n",
    "\n",
    "# List of filenames (assuming the files are named ED1, ED2, ED3, ED4)\n",
    "filenames = ['rho_z_dataset_1.txt', 'rho_z_dataset_3.txt', 'rho_z_dataset_5.txt', 'rho_z_dataset_6.txt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0799f109-55ac-485d-88c6-670e6bd7ecc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area under the curve for rho_z_dataset_1.txt: 0.8064858839790601\n",
      "Area under the curve for rho_z_dataset_3.txt: 3.183657700061111\n",
      "Area under the curve for rho_z_dataset_5.txt: 4.342536042859978\n",
      "Area under the curve for rho_z_dataset_6.txt: 2.0051748065153614\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To store areas under the curves\n",
    "areas = []\n",
    "\n",
    "# Plotting the data and calculating area for each dataset\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "for filename in filenames:\n",
    "    # Load data from each file\n",
    "    znew, rho = load_data(filename)\n",
    "    \n",
    "    # Plot the dataset\n",
    "    plt.plot(znew, rho, label=filename)\n",
    "    \n",
    "    # Create rho_func from the loaded data\n",
    "    rho_func = create_rho_func(znew, rho)\n",
    "    \n",
    "    # Define the rho_sub value (you can modify this as needed, e.g., 0 for baseline)\n",
    "    rho_sub = 0.334\n",
    "    \n",
    "    # Calculate the area using rho_2D\n",
    "    area = rho_2D(rho_func, rho_sub, -200, -10)\n",
    "    areas.append(area)\n",
    "\n",
    "# Plot configurations\n",
    "plt.title('Rho vs Znew for Multiple Datasets')\n",
    "plt.xlabel('Znew')\n",
    "plt.ylabel('Rho')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.axvline(x=-10)\n",
    "# # Show plot\n",
    "# plt.show()\n",
    "\n",
    "# Output the areas\n",
    "for filename, area in zip(filenames, areas):\n",
    "    print(f\"Area under the curve for {filename}: {area}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63b10b68-b82d-4215-a23a-faa2b75fd12f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
